Electrocardiogram (ECG) signal based authentication apparatus and method

ABSTRACT

An authentication apparatus includes one or more processors configured to temporally implement a neural network, used to extract a feature value from hidden nodes, that is connected to input nodes to which an electrocardiogram (ECG) signal is input so as to share a weight set with the input nodes, and to match the ECG signal and the extracted feature value to a user for registration.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Divisional of U.S. patent application Ser. No.15/246,712 filed Aug. 25, 2016 which claims the benefit under 35 USC119(a) of Korean Patent Application No. 10-2016-0012179 filed on Feb. 1,2016 in the Korean Intellectual Property Office, the entire disclosureof which is incorporated herein by reference for all purposes.

BACKGROUND 1. Field

The following description relates to electrocardiogram (ECG) signalbased authentication technology. For example, the following descriptionrelates to an electrocardiogram (ECG) signal based authenticationapparatus. The following description also relates to anelectrocardiogram (ECG) signal based authentication method.

2. Description of Related Art

Wearable devices implemented in wearable forms, for example, glasses, awatch, and clothing have been commercialized. For example, users maycontact the wearable devices to acquire desired information from thewearable devices. The wearable devices may acquire biosignals such aselectroencephalogram (EEG) signals and electromyogram (EMG) signals ofthe user. In general, a wearable device may include an authenticationsystem for verifying whether a user in contact with the wearable deviceis a registered user. The wearable device may identify the user byreceiving a fingerprint, a voice, or user information input through atouch interface, thereby authenticating the user.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

In one general aspect, an authentication apparatus includes one or moreprocessors configured to temporally implement a neural network, used toextract a feature value from hidden nodes, that is connected to inputnodes to which an electrocardiogram (ECG) signal is input so as to sharea weight set with the input nodes, and match the ECG signal and theextracted feature value to a user for registration.

The authentication apparatus may further include a memory, wherein theprocessor uses the memory to match the ECG signal and the extractedfeature value to the user for registration.

The neural network may include the hidden nodes connected with inputnode sets, each of the input node sets including a number of input nodesconnected through a machine learning.

The neural network may be configured to sequentially connect the hiddennodes to the input node sets.

The one or more processors may further include a signal separatorconfigured to separate at least one interval signal of a P wave, a QRSwave, and a T wave from the ECG signal, and the neural network may beconfigured to apply the weight set determined based on the at least oneinterval signal to calculate an output value.

The neural network may include at least one of a first sub-neuralnetwork having a first weight set corresponding to the P wave, a secondsub-neural network having a second weight set corresponding to the QRSwave, or a third sub-neural network having a third weight setcorresponding to the T wave.

A number of nodes included in each of the three sub-neural networks andweights to be assigned to the nodes may be determined based on a resultof machine learning.

The machine learning may be performed using a Siamese neural network.

The neural network may be configured to select an upper value in a rangefrom feature values corresponding to the hidden nodes.

In another general aspect, an authentication apparatus includes acalculator configured to calculate a first output value corresponding toan input electrocardiogram (ECG) signal and second output valuescorresponding to a reference ECG signal based on a neural network atleast temporally implemented by one or more processors, an extractorconfigured to extract a number of second output values from the secondoutput values based on the first output value, and a determinerconfigured to determine whether to authenticate a user associated withthe input ECG signal based on a ratio of a number of the extractedsecond output values that are associated with a registered user to atotal number of the extracted second output values.

The reference ECG signal may include an ECG signal associated with anunidentified user and an ECG signal associated with the registered user,and the calculator may be further configured to calculate second outputvalues corresponding to the unidentified user and the registered user.

The extractor may be configured to extract the number of second outputvalues in a descending order of similarities with the first outputvalue.

The extractor may be configured to repetitively extract second outputvalues from the second output values, a number of the extracted secondoutput values being different for each extraction, and the determinermay be configured to determine whether to authenticate the user based onthe ratio through a repetitive calculation.

The determiner may be further configured to authenticate the userassociated with the input ECG signal to be the registered user inresponse to the ratio of the number of the extracted second outputvalues associated with the registered user to the total number of theextracted second output values having a highest value.

The determiner may be further configured to authenticate the userassociated with the input ECG signal to be the registered user inresponse to the ratio of the number of the extracted second outputvalues associated with the registered user to the total number of theextracted second output values being higher than or equal to athreshold.

The authentication apparatus may further include a memory configured tostore the reference ECG signal, wherein, in response to the user beingauthenticated as the registered user, the determiner is configured tostore the input ECG signal in the memory as a part of the reference ECGsignal in association with the registered user.

The memory may be configured to store ECG signals corresponding todifferent points in time as the reference ECG signal.

In another general aspect, a wearable device includes a sensorconfigured to acquire an input electrocardiogram (ECG) signal from abody of a user in contact with the wearable device, a processorconfigured to calculate a first output value corresponding to the inputECG signal and second output values corresponding to a reference ECGsignal based on a neural network, extract a number of second outputvalues from the second output values based on the first output value,and authenticate the user based on a ratio of a number of the extractedsecond output values that are associated with a registered user to atotal number of the extracted second output values, and a displayconfigured to output an authentication result to the user.

The processor may be further configured to extract the number of secondoutput values from the second output values in a descending order ofsimilarities with the first output value.

The processor may be configured to determine the user to be theregistered user in response to the ratio of the extracted second outputvalues associated with the registered user being higher than or equal toa threshold.

Other features and aspects will be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart illustrating an example of an authenticationapparatus for registering an electrocardiogram (ECG) signal inaccordance with an embodiment.

FIGS. 2A and 2B are diagrams illustrating an example of extracting afeature value from an input ECG signal in accordance with an embodiment.

FIG. 2C is a diagram illustrating an example of a sub-neural network inaccordance with an embodiment.

FIG. 2D is a diagram illustrating an example of extracting a featurevalue based on a neural network in accordance with an embodiment.

FIG. 3 is a diagram illustrating an example of a machine learning of aneural network in accordance with an embodiment.

FIGS. 4A and 4B are diagrams illustrating an example of generating asub-neural network for each interval signal of an ECG signal inaccordance with an embodiment.

FIG. 5 is a block diagram illustrating an example of an authenticationapparatus in accordance with an embodiment.

FIG. 6 is a flowchart illustrating an example of an operation of anauthentication apparatus in accordance with an embodiment.

FIG. 7 is a diagram illustrating an example of an operation of a Siameseneural network in accordance with an embodiment.

FIG. 8 is a diagram illustrating an example of an authenticationapparatus applying a k-nearest neighborhood algorithm in accordance withan embodiment.

FIG. 9 is a block diagram illustrating another example of anauthentication apparatus in accordance with an embodiment.

Throughout the drawings and the detailed description, the same referencenumerals refer to the same elements. The drawings may not be to scale,and the relative size, proportions, and depiction of elements in thedrawings may be exaggerated for clarity, illustration, and convenience.

DETAILED DESCRIPTION

The following detailed description is provided to assist the reader ingaining a comprehensive understanding of the methods, apparatuses,and/or systems described herein. However, various changes,modifications, and equivalents of the methods, apparatuses, and/orsystems described herein will be apparent to one of ordinary skill inthe art. The sequences of operations described herein are merelyexamples, and are not limited to those set forth herein, but may bechanged as will be apparent to one of ordinary skill in the art, withthe exception of operations necessarily occurring in a certain order.Also, descriptions of functions and constructions that are well known toone of ordinary skill in the art may be omitted for increased clarityand conciseness.

The features described herein may be embodied in different forms, andare not to be construed as being limited to the examples describedherein. Rather, the examples described herein have been provided so thatthis disclosure will be thorough and complete, and will convey the fullscope of the disclosure to one of ordinary skill in the art.

Terms such as first, second, A, B, (a), (b), and the like may be usedherein to describe components. However, each of these terms is not usedto define an essence, order or sequence of a corresponding component butis used merely to distinguish the corresponding component from othercomponent(s). For example, a first component may be referred to a secondcomponent, and similarly the second component may also be referred to asthe first component, but these terms are used to indicate that the firstcomponent and the second component are separate components.

It is to be noted that if it is described in the specification that onecomponent is “connected,” “coupled,” or “joined” to another component, athird component may be “connected,” “coupled,” and “joined” between thefirst and second components, although the first component may bedirectly connected, coupled or joined to the second component. Inaddition, it is to be noted that if it is described in the specificationthat one component is “directly connected” or “directly joined” toanother component, a third component may not be present therebetween.Likewise, expressions, for example, “between” and “immediately between”and “adjacent to” and “immediately adjacent to” may also be construed asdescribed in the foregoing with respect to their meaning with respect tothe relationship between the terms they are used to pertain to.

The terminology used herein is for the purpose of describing particularexamples only, and is not to be used to limit the disclosure. As usedherein, the terms “a,” “an,” and “the” are intended to include theplural forms as well, unless the context clearly indicates otherwise. Asused herein, the terms “include,” “comprise,” and “have” specify thepresence of stated features, numbers, operations, elements, components,and/or combinations thereof, but do not preclude the presence oraddition of one or more other features, numbers, operations, elements,components, and/or combinations thereof.

Unless otherwise defined, all terms, including technical and scientificterms, used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this disclosure pertains. Terms,such as those defined in commonly used dictionaries, are to beinterpreted as having a meaning that is consistent with their meaning inthe context of the relevant art, and are not to be interpreted in anidealized or overly formal sense unless expressly so defined herein.

The following examples are applied to recognize an electrocardiogram(ECG) signal of a user. Subsequently, an operation of recognizing theECG signal of the user includes an operation of recognizing the ECGsignal to verify or identify the user. For example, an operation ofauthentication of the user includes an operation of determining whetherthe user is a pre-registered user. In this example, a result of theoperation of authentication the user is to be output as true or false.The authentication produces a true result when the identity of the usermatches the pre-registered identity and produces a false result when theidentity of the user does not match the pre-registered user.

An operation of identifying the user includes an operation ofdetermining a user corresponding to the user from among a plurality ofregistered users. In this example, a result of the operation ofidentifying the user is to be output as an identification (ID) of one ofthe plurality of registered users. For example, the identification maybe a numeric ID or an alphanumeric ID. When the user does not correspondto any one of the plurality of the registered users, a signal indicatingthat the user is not identified may be output. The following examplesare implemented through, for example, using a wearable device includinga sensor configured to acquire an ECG signal of a body of a user and adisplay configured to output an authentication result of the user basedon the acquired signal.

Hereinafter, example embodiments are described in further detail withreference to the accompanying drawings. Regarding the reference numeralsassigned to the elements in the drawings, it is to be noted that thesame elements are designated by the same reference numerals and repeateddescriptions are omitted.

FIG. 1 is a diagram illustrating an example of an authenticationapparatus for registering an ECG signal in accordance with anembodiment.

In an example, a neural network indicates a computing device configuredto perform a machine learning based on input data. The input datacorresponds to a biosignal of a user acquired through using a sensingdevice. For example, the sensing device is implemented as a wearabledevice. In such an example, the biosignal is an ECG signal. The neuralnetwork is at least temporally implemented by at least one processorincluded in an authentication apparatus, such that the neural networkchanges over time based on the operation provided by the at least oneprocessor. In the following examples, nodes are understood to beartificial neurons included in the neural network.

Referring to FIG. 1, a method 100 of registering an ECG signal isperformed by an authentication apparatus. Thus, in an example, thefollowing method steps are performed by such an authenticationapparatus. In operation 110, the method acquires a plurality of ECGsignals associated with a user. An ECG signal represents, for example,an ECG period including a P wave, a QRS wave, and a T wave. In such anexample, the P wave represents atrial depolarization, the QRS wave orQRS complex represents ventricular depolarization, and the T waverepresents ventricular repolarization. Thus, in operation 110, themethod acquires the plurality of ECG signals corresponding to aplurality of ECG periods.

In operation 120, the method separates the P wave, the QRS wave, and theT wave from each signal of the plurality of ECG signals. As an example,when the method acquires 100 ECG signals of the user in operation 110,the method extracts 100 P waves, 100 QRS waves, and 100 T waves from the100 ECG signals in operation 120.

In operation 130, the method extracts a feature value from the pluralityof ECG signals based on the neural network. The feature value indicates,for example, a result value of the machine learning performed based onthe neural network. In an example, a similar feature value is obtainedwith respect to the same ECG signals and different feature values areobtained with respect to different ECG signals. The authenticationapparatus extracts a feature value from an ECG signal of a registereduser and determine whether a predetermined ECG signal is a registeredECG signal associated with the registered user by comparing thepredetermined ECG signal and the extracted ECG signal. A process of thedetermining is described in further detail later with reference to thedrawing below.

In operation 140, the method stores the plurality of ECG signals and theextracted feature values. As an example, the method stores a pluralityof feature values corresponding to the plurality of ECG signals. Asanother example, the method acquires a representative ECG signal byperforming normalization on the plurality of ECG signals and stores afeature value corresponding to the representative ECG signal. As stillanother example, the method calculates the plurality of feature valuescorresponding to the plurality of ECG signals and stores an averagevalue of the plurality of feature values as the representative featurevalue. The aforementioned operations of storing the ECG signals and thefeature value in the authentication apparatus for a user authenticationare described as an example only. Thus, the present examples are not tobe taken as being limited thereto.

A process in which the authentication apparatus extracts a feature valueusing an input layer node and a hidden layer node included in a neuralnetwork will be described in detail later with reference to the drawingbelow.

FIGS. 2A and 2B are diagrams illustrating an example of extracting afeature value from an input ECG signal in accordance with an embodiment.

FIG. 2A illustrates a graph of an ECG signal input to an authenticationapparatus. In the graph, an X axis represents a time in seconds and a Yaxis represents an amplitude of the input ECG signal based on a unit ofmillivolts (mV). For example, the input ECG signal is a signal obtainedby preprocessing a biosignal of a user. Various sources andcharacteristics of such a biosignal have been discussed further, above.The authentication apparatus filters the biosignal to remove a noisesignal and detects a peak. In an example, the authentication apparatusdetects an R peak of a QRS wave of the ECG signal. Also, theauthentication apparatus generates a template of the ECG signal based onthe peak, performs normalization, and detects an outlier. As thepreprocessing of the authentication apparatus performed on the input ECGsignal may be performed using existing techniques, descriptions of suchare omitted for brevity.

The authentication apparatus generates ECG signals, for example, a firstsampled ECG signal 211 to a ninth sampled ECG signal 219. The ECGsignals are sampled to correspond to preset time intervals based on theinput ECG signal. FIG. 2A illustrates nine sampled ECG signals as anexample. Thus, the number of sampled ECG signals is not construed asbeing limited to the example described herein. Accordingly, theauthentication apparatus also operates based having the number ofsampled ECG signals varying according to another appropriate example.

FIG. 2B illustrates an input layer node 220 and a hidden layer node 230included in the authentication apparatus. The input layer node 220includes at least one input node, for example, a first input node 221 toa ninth input node 229. The first sampled ECG signals to the ninthsampled ECG signal 219 are input to the first input node 221 to theninth input node 229, respectively. For example, the first sampled ECGsignal 211 is input to the first input node 221. The hidden layer node230 includes at least one hidden node, for example, a first hidden node231 to a seventh hidden node 237.

The authentication apparatus groups a preset number of input nodes, forexample, the first input node 221, a second input node 222, and a thirdinput node 223 into a first input node set. Similarly, theauthentication apparatus groups the second input node 222, the thirdinput node 223, and a fourth input node 224 into a second input nodeset. Also, the authentication apparatus groups a seventh input node 227,an eighth input node 228, and the ninth input node 229 into a thirdinput node set. FIG. 2B illustrates that three input nodes are groupedinto one input node set as an example. However, the present disclosureis not limited to the aforementioned example and thus, variousalternative groupings of input nodes are applicable in other examples.

The authentication apparatus calculates an output value corresponding tothe input ECG signal based on the neural network. That is, the input ECGis fed into the neural network, which has learned ways to consider suchinputs to associate the inputs with user identifications. The outputvalue indicates, for example, a feature value used to identify orauthenticate a user. The authentication apparatus assigns an identicalweight set to the first input node set and calculates an output valuecorresponding to a first hidden node, for example, the hidden node 231.Similarly, the authentication apparatus assigns the identical weight setto the second input node set and the third input node set and calculatesoutput values corresponding to a second hidden node, for example, thehidden node 232 and a third hidden node, for example, the hidden node233.

To calculate the output value corresponding to the first hidden node231, a weight set including weights of 0.3, 0.5, and 0.2 is applied tothe first sampled ECG signal 211, the second sampled ECG signal 212, andthe third sampled ECG signal 213 corresponding to the first input nodeset. For example, 0.3 is applied to the first sampled ECG signal 211,0.5 is applied to the second sampled ECG signal 212, and 0.2 is appliedto the third sampled ECG signal 213. Similarly, to calculate the outputvalue corresponding to the second hidden node 232, the same weight setincluding 0.3, 0.5, and 0.2 is applied to the second sampled ECG signal212, the third sampled ECG signal 213, and the fourth sampled ECG signal214 corresponding to the second input node set. Through this, 0.3 isapplied to the second sampled ECG signal 212, 0.5 is applied to thethird sampled ECG signal 213, and 0.2 is applied to the fourth sampledECG signal 214. In such an example, the first hidden node 231 throughthe seventh hidden node 237 are connected to the first input node 221through the ninth input node 229 based on a weight set shared betweenthe nodes, as discussed above, which allows machine learning to takeplace in a way that facilitates recognition by using the applied weightssuch that the sampled ECG signals are successfully usable to generaterecognition results.

The ECG signal varies based on a state of a user's heart, such as aphysiological state of the user's heart. For example, an ECG assessesthe electrical and muscular functions of the heart. A position of a peakin an ECG signal corresponding to one period may vary based on the stateof user, for example, an excited state, a lack of sleep, and anexcessive consumption of caffeine. In an example, the authenticationapparatus groups input nodes in a neural network into input node setsand applies an identical weight set to the input node sets. For example,nodes from among a plurality of hidden nodes included in the neuralnetwork share the identical weight set to be connected with the inputnodes. When an ECG signal input to a plurality of input nodescorresponds to a preset time interval, the authentication apparatusoutputs an identical output value or an output value in a preset errorrange as a feature value. Thus, the authentication apparatus that usessuch an approach more stably performs a user authentication irrespectiveof noise or a change in the state of user, in that it is able to use theneural network to learn which certain feature patterns are to beassociated with which authentication outcomes.

FIG. 2C is a diagram illustrating an example of a sub-neural network inaccordance with an embodiment.

FIG. 2C illustrates a sub-neural network for extracting a feature value.The sub-neural network includes the input layer node 220 including aplurality of input nodes, for example, the first input node 221 to theninth input node 229, and the hidden layer node 230 including aplurality of hidden nodes, for example, the first hidden node 231 to theseventh hidden node 237. As discussed in the foregoing explanation, anidentical weight set is shared in the hidden layer node 230 and thehidden layer node 230 is connected to the input layer node 220. Becausethe descriptions of FIGS. 2A and 2B are applicable here, repeateddescriptions related to FIG. 2C are omitted for brevity.

In FIG. 2C, the sub-neural network corresponds to at least one intervalsignal of a P wave, a QRS wave, and a T wave included in an ECG signal.These various waveforms are discussed further, above. In such anexample, an authentication apparatus includes at least one sub-neuralnetwork. For example, the authentication apparatus includes at least oneof a first sub-neural network having a first weight set corresponding tothe P wave, a second sub-neural network having a second weight setcorresponding to the QRS wave, and a third sub-neural network having athird weight set corresponding to the T wave. By including informationrelated to these weight sets, an embodiment is able to achieve improvedanalysis results of the ECG signal.

For example, the ECG signal is separated into the P wave, the QRS wave,and the T wave based on a movement of a heart dissimilarly to othertypes of signals. For example, the authentication apparatus uses arelatively small number of internal nodes including sub-neural networkscorresponding to the P wave, the QRS wave, and the T wave, eachrepresenting a different characteristic of the ECG signal, therebyenhancing an accuracy of an authentication.

The method performs a max pooling on a plurality of feature valuescorresponding to the hidden layer node 230 in operation 240. Forexample, the authentication apparatus performs a max pooling on aplurality of feature values corresponding to the hidden layer node 230in operation 240. As an example, the authentication apparatus extractsan upper value in a preset range as the feature value. As anotherexample, the authentication apparatus extracts a first feature valuecorresponding to a highest value from among the plurality of featurevalues as a feature value corresponding to an input ECG signal. Based ona time shifting feature of the ECG signal, the authentication apparatusis implemented such that the identical weight set is shared in thehidden layer node 230. Thus, the first hidden node 231 to the seventhhidden node 237 having a highest feature value output a feature valueassociated with a sampling interval during which the ECG signal isactually input. Accordingly, the authentication apparatus determines anoutput value by performing the max pooling on an output value of thehidden layer node 230 in operation 240 and thus, stably outputs thefeature value corresponding to the input ECG signal despite a timeshifting.

Therefore, FIG. 2C illustrates a process of extracting four featurevalues corresponding to the input ECG signal in an output layer node 250as an example. Accordingly, the number of feature values varying basedon a machine learning of the neural network is to be output as thefeature value corresponding to the input ECG signal.

FIG. 2D is a diagram illustrating an example of extracting a featurevalue based on a neural network in accordance with an embodiment.

FIG. 2D illustrates a neural network 260 included in an authenticationapparatus. In the example of FIG. 2D, the neural network 260 includes aninput layer node 261 that receives an input ECG signal and an outputlayer node 262 that outputs a feature value corresponding to the inputECG signal. In such an example, the neural network 260 includes threesub-neural networks. For example, the neural network 260 includes afirst sub-neural network 271, a second sub-neural network 272, and athird sub-neural network 273. In this example, the first sub-neuralnetwork 271 extracts a feature value of a P wave from the input ECGsignal. The second sub-neural network 272 extracts a feature value of aQRS wave from the input ECG signal. The third sub-neural network 273extracts a feature value of a T wave from the input ECG signal.Accordingly, the neural network and sub-neural network operate so as toprovide specialized analysis of various waveforms of the input ECGsignal. Also, the feature value of the input ECG signal is calculated byfully connecting and combining the feature values of the firstsub-neural network 271, the second sub-neural network 272, and the thirdsub-neural network 273. A number of nodes included in each of the threesub-neural networks and weights to be assigned to the nodes aredetermined accordingly based on a result of machine learning. Themachine learning performed by the authentication apparatus to perform anauthentication is described further with reference to the drawing below.

FIG. 3 is a diagram illustrating an example of a machine learning of aneural network in accordance with an embodiment.

FIG. 3 illustrates a process of machine learning that is performed by anauthentication apparatus in order to extract a feature value. In theexample of FIG. 3, the authentication apparatus extracts a feature valuefor authenticating a user by using a Siamese neural network, forexample, a first neural network 321 and a second neural network 322.Here, the Siamese neural network refers to identical, complementaryneural networks. For example, the first neural network 321 and thesecond neural network 322 share attributes such as the number of nodes,the weight, and a connection relationship.

In the example of FIG. 3, a first ECG signal X1 311 is input to thefirst neural network 321. Likewise, in the example of FIG. 3, a secondECG signal X2 312 is input to the second neural network 322. A firstfeature value Y1 331 and a second feature value Y2 322 corresponding tothe first ECG signal X1 311 and the second ECG signal X2 312 input tothe first neural network 321 and the second neural network 322 arecalculated as output values by using the neural networks. Also, theauthentication apparatus previously stores identification information ofa user associated with the first ECG signal X1 311 and the second ECGsignal X2 312. Based on such identification information, theauthentication apparatus performs the machine learning such that adifference value |Y1-Y2| 340 between feature values 331 and 332 is foundto be relatively small when ECG signals are acquired from the same user.Also, the authentication apparatus performs the machine learning suchthat the difference value |Y1-Y2| 340 between the feature values 331 and332 is found to be relatively large when ECG signals are acquired fromdifferent users. These similarities and differences are derived in thismanner because the machine learning adapts the neural network to provideresults in this manner. Accordingly, based on a result of the machinelearning, a feature value for quickly and accurately determining an ECGsignal corresponding to each registered user is verified.

FIGS. 4A and 4B are diagrams illustrating an example of generating asub-neural network for each interval signal of an ECG signal inaccordance with an embodiment.

FIG. 4A illustrates a graph of a P wave 421, a QRS wave 422, a T wave423, and an input ECG signal 410 that are acquired from a biosignalinput to an authentication apparatus. In the graph, an X axis representsa time in seconds and a Y axis represents an amplitude having a unit ofmV. The authentication apparatus extracts the P wave 421, the QRS wave422, and the T wave 423 from the input ECG signal 410 corresponding toone period of the ECG signal 410. For example, the authenticationapparatus extracts the QRS wave 422 using an average value of QRSintervals based on a position of an R peak and extracts interval signalsseparated by the QRS wave 422 as the P wave 421 and the T wave 423.However, the present disclosure is not limited to such an example. Thus,the authentication apparatus extracts each of the P wave 421, the QRSwave 422, and the T wave 423 in different ways based on variousextraction methods.

FIG. 4B illustrates a first sub-neural network 431 corresponding to theP wave 421, a second sub-neural network 432 corresponding to the QRSwave 422, and a third sub-neural network 433 corresponding to the T wave423. In the example of FIG. 4B, the authentication apparatus inputs theP wave 421, the QRS wave 422, and the T wave 423 previously extractedfrom the input ECG signal 410 to input nodes of each of the firstsub-neural network 431, the second sub-neural network 432, and the thirdsub-neural network 433. For example, the inputs of the P wave 421, theQRS wave 422, and the T wave 423 were previously extracted from theinput ECG signal 410 as per FIG. 4A. Each of the first sub-neuralnetwork 431, the second sub-neural network 432, and the third sub-neuralnetwork 433 calculates an output value of a corresponding hidden layernode by applying a different weight to the P wave 421, the QRS wave 422,and the T wave 423. Hence, the machine learning facilitates processingthese parts of the input ECG signal 410 in a facilitated manner withbetter performance. In this example, the example of FIGS. 2A and 2Billustrating that the identical weight set is applied to each of theinput node sets is also applicable for understanding the example.

For example, the authentication apparatus generates the first sub-neuralnetwork 431, the second sub-neural network 432, and the third sub-neuralnetwork 433 corresponding to the P wave 421, the QRS wave 422, and the Twave 423, respectively. The authentication apparatus generates the firstsub-neural network 431, the second sub-neural network 432, and the thirdsub-neural network 433 as being independent of one another through amachine learning, such as based on a prestored reference ECG signal. Thereference ECG signal includes, for example, an ECG signal correspondingto an unidentified user as well as an ECG signal corresponding to aregistered user. In such an example, the unidentified user may be a userdiffering from the registered user.

Based on the machine learning, the authentication apparatus determines aconnection relationship of nodes, a number of hidden layer nodes, a sizeof a weight for an input node set, and a number of input layer nodes foreach of the first sub-neural network 431, the second sub-neural network432, and the third sub-neural network 433. In this example, each of thefirst sub-neural network 431, the second sub-neural network 432, and thethird sub-neural network 433 has a simple connection relationship ofnodes when compared to a neural network for processing the input ECGsignal 410 overall. Also, in such an example, the number of hidden layernodes connecting an input layer node and an output layer node in aneural network may decrease. The authentication apparatus generates thefirst sub-neural network 431, the second sub-neural network 432, and thethird sub-neural network 433 as independently corresponding to each ofthe P wave 421, the QRS wave 422, and the T wave 423, thereby avoidingan overfitting output value which may be calculated based on theprestored reference ECG signal, which possibly occurs in otherapproaches.

FIG. 5 is a block diagram illustrating an example of an authenticationapparatus in accordance with an embodiment.

Referring to the embodiment of FIG. 5, an authentication apparatus 500includes a neural network 510, a memory 520, and a signal separator 530.For example, the authentication apparatus 500 performs a machinelearning to authenticate a user based on a prestored reference ECGsignal. For example, based on the machine learning, the neural network510 groups a preset number of input nodes into an input node set andcalculates an output value by applying an identical weight set assignedto the input node set. However, it is to be noted that theauthentication apparatus 500 is not to be limited to these elementsalone, and in other embodiments the authentication apparatus 500 mayinclude additional elements, as appropriate.

The authentication apparatus 500 is implemented as, for example, aportable electronic device. The portable electronic device may beimplemented as, for example a laptop computer, a mobile phone, a smartphone, a tablet PC, a mobile internet device (MID), a personal digitalassistant (PDA), an enterprise digital assistant (EDA), a digital stillcamera, a digital video camera, a portable multimedia player (PMP), apersonal navigation device or portable navigation device (PND), ahandheld consol, an e-book, and a smart device. The smart device may beimplemented to be, for example, a smart watch and a smart band. However,these are merely examples of possible candidate devices that areportable electronic devices or smart devices, and other examples includealternative types of devices, as appropriate.

The neural network 510 groups an input node included in the neuralnetwork 510 into an input node set to which an identical weight set isassigned. In an example, the neural network 510 groups input nodesreceiving an ECG signal within a predetermined time range into the inputnode set. Also, the neural network 510 generates a hidden layer nodecorresponding to the input node set. Furthermore, the neural network 510matches the hidden layer node and the output value, appropriately.

In another example, the neural network 510 connects a plurality ofhidden nodes to input node sets that include a preset number of inputnodes, determined based on the machine learning. Thus, the hidden nodesof the plurality of hidden nodes share the identical weight set and areconnected to the input node sets, respectively.

Also, the neural network 510 extracts a feature value corresponding tothe hidden layer node by applying a weight to an ECG signal input toeach input node. For example, one input node set matches one hiddenlayer node. In this manner, the neural network 510 sequentially connectsthe plurality of hidden nodes to the input node sets.

In the embodiment of FIG. 5, the memory 520 registers a feature valuecalculated using an input ECG signal associated with a user and theneural network 510 by matching the feature value and the user, asdiscussed further, above. Also, the memory 520 stores a plurality ofreference ECG signals of users differing from a registered user. Thesereference ECG signals of users differing from a registered user helpdisambiguate signals by clarifying when signals are the same and whenthey are different.

In the embodiment of FIG. 5, the signal separator 530 separates at leastone interval signal of a P wave, a QRS wave, and a T wave from the inputECG signal. For example, the signal separator 530 extracts a position ofan R peak from the input ECG signal. Also, the signal separator 530extracts, as the QRS wave, an upward wave including the R peak, adownward wave extracted after the upward wave, and a downward waverecorded before the upward wave. These operations are described as anexample. However, in other examples, the signal separator 530 alsoseparates at least one interval signal of the P wave, the QRS wave, andthe T wave from the input ECG signal based on various signal separationmethods. As discussed above, various analytical techniques helpfacilitate such signal separation methods.

For example, the neural network 510 includes a sub-neural networkcorresponding to at least one interval signal. In an example, the neuralnetwork 510 includes three independent neural networks having aconnection relationship, a size of a parameter, and the number of nodescorresponding to each of the P wave, the QRS wave, and the T wave.

In the embodiment of FIG. 5, the neural network 510 sets an initialsetting value of the neural network 510 based on the prestored referenceECG signal. For example, the neural network 510 sets at least one of asize of the weight applied to the input node set, the number of inputnodes included in the input node set, and a distance between the inputnodes to be the initial setting value. Also, the neural network 510adjusts the initial setting value based on a registered ECG signal thatcorresponds to the registered user. The neural network 510 additionallyperforms the machine learning based on the registered ECG signal afterthe machine learning is performed based on the reference ECG signal. Inthe embodiment of FIG. 5, the authentication apparatus 500 performs afine tuning operation based on the registered ECG signal correspondingto the registered user, thereby quickly and accurately authenticatingthe registered user.

FIG. 6 is a flowchart illustrating an example of an operation of anauthentication apparatus in accordance with an embodiment. For example,the operations of FIG. 6 may be performed by an authenticationapparatus.

Referring to the embodiment of FIG. 6, in operation 610, a methodcalculates output values corresponding to an input ECG signal and areference ECG signal based on a neural network. The output values eachindicate a feature value matching a registered ECG signal and that arestored in a memory of the authentication apparatus in advance. In anexample, the method calculates the output values based on a first neuralnetwork corresponding to the input ECG signal and a second neuralnetwork corresponding to the reference ECG signal. The first neuralnetwork and the second neural network are, for example, Siamese neuralnetworks sharing weights assigned to internal nodes with each other. TheSiamese neural networks used in a process of user authentication aredescribed further in detail with reference to the drawing below. In thisexample, the input ECG signal is an ECG signal of a user that is incontact with the authentication apparatus. Also, for example, thereference ECG signal is an ECG signal previously stored in theauthentication apparatus. In an example, the reference ECG signal isassociated with one of a registered user and an unidentified usercorresponding to a user differing from the registered user. Depending onan example, the authentication apparatus has one registered user oralternatively has a plurality of registered users.

In operation 610, the authentication apparatus acquires a plurality ofinput ECG signals of a target user on which a user authentication is tobe performed based on an ECG signal, and extracts a P wave, a QRS wave,and a T wave from each of the plurality of input ECG signals. Since thedescriptions related to operations 110 and 120 of FIG. 1 are alsoapplicable here, repeated descriptions will be omitted for brevity.

In operation 620, the method calculates a similarity between thecalculated output values. For example, the method calculates thesimilarity between the output values by comparing output valuescorresponding to the input ECG signal and the reference ECG signal. Suchsimilarity may be calculated by using, for example, a norm, aroot-mean-square (RMSE), a correlation, and a cosine similarity.However, these are only example metrics, and other methods of assessingsimilarity are used in other examples.

In operation 630, the method authenticates a user based on the input ECGsignal and a ratio of a reference ECG signal that is included in apreset similarity range to a registered user. For example, the methodextracts the input ECG signal and the reference ECG signal included inthe preset similarity range based on a k-nearest neighborhood algorithm.However, this is only a sample of an algorithm that may be used toextract the input ECG signal and the reference ECG signal and otherappropriate algorithms may also be used.

In an example, the method calculates a ratio of the reference ECG signalto a registered ECG signal that is associated with the registered user.When the calculated ratio is a highest ratio from among ratios of otherusers, a user in contact with the authentication apparatus isauthenticated to be the registered user. That is, the method considersall of the possible identities of previously registered users,determines which is most likely to be the registered user, anddetermines such a user to be the registered user.

In another example, when the ratio of the reference ECG signal to theregistered ECG signal associated with the registered user is higher thanor equal to a threshold, the method authenticates the user in contactwith the authentication apparatus to be the registered user. Thus, insuch approach, the method establishes that it is sufficiently likelythat the user is the correctly authenticated user. The k-nearestneighborhood algorithm used in the authentication apparatus is describedin further detail with reference to the drawing below.

FIG. 7 is a diagram illustrating an example of an operation of a Siameseneural network in accordance with an embodiment.

Referring to the embodiment of FIG. 7, an authentication apparatusinputs a prestored reference ECG signal 711 and an input ECG signal 712acquired from a user into a Siamese neural network. For example, thereference ECG signal 711 is input to a first neural network 721 and theinput ECG signal 712 is input to a second neural network 722. In such anexample, the first neural network 721 and the second neural network 722included in the Siamese neural network are neural networks sharing atleast one of a connection relationship of nodes, the number of internalnodes, a size of weight, and a size of a parameter.

The first neural network 721 calculates G_(W)(X₁) 731 as an output valueof the first neural network 721. Similarly, the second neural network722 calculates G_(W)(X₂) 732 as an output value of the second neuralnetwork 722. In this example, the authentication apparatus stores aplurality of ECG signals as the reference ECG signal. The reference ECGsignal is an ECG signal associated with a registered user, and is alsoan ECG signal associated with a user differing from the registered user.Hence, the reference ECG signal includes information about theregistered user as well as other users. Thus, the first neural network721 calculates a plurality of values of G_(W)(X₁) 731 as the outputvalue 731 of the first neural network 721. The first neural network 721and the second neural network 722 are included in the Siamese neuralnetwork and thus, calculate output values of which similaritiesincreases according to an increase in similarities of input data.Accordingly, the output values are appropriate values for assessingwhether the reference ECG signal 711 and the input ECG signal 712 arerelated. For example, when the reference ECG signal 711 is similar tothe input ECG signal 712, a similarity 740 between G_(W)(X₁) 731 andG_(W)(X₂) 732 is relatively high, such that the absolute value of thedifference between G_(W)(X₁) 731 and G_(W)(X₂) 732 is small. Also, whenthe reference ECG signal 711 is different from the input ECG signal 712,the similarity 740 between G_(W)(X₁) 731 and G_(W)(X₂) 732 is relativelylow, and the difference between these values is relatively great.

In an example, the authentication apparatus applies a k-nearestneighborhood algorithm to one value of G_(W)(X₂) 732 corresponding tothe input ECG signal 712 and a plurality of values of G_(W)(X₁) 731corresponding to a plurality of reference ECG signals including thereference ECG signal 711. Accordingly, the authentication apparatusdetermines whether a user associated with the input ECG signal 712 isthe registered user, based on the results of the k-nearest neighborhoodalgorithm. The foregoing example is described in further detail withreference to the drawing below.

FIG. 8 is a diagram illustrating an example of an authenticationapparatus applying a k-nearest neighborhood algorithm in accordance withan embodiment.

FIG. 8 illustrates a plurality of output values acquired from each of aninput ECG signal and a reference ECG signal. For example, anauthentication apparatus acquires a first output value 810 based on aninput ECG signal of a user. The user is, for example, a user that is incontact with the authentication apparatus, such that the user is to beauthenticated. The authentication apparatus acquires a plurality ofsecond output values including, for example, an output value 820 andoutput values 830, 840, and 850 based on a prestored reference ECGsignal. These output values are illustrated using appropriate symbols inthe example of FIG. 8. As presented in the foregoing, the reference ECGsignal includes an ECG signal associated with a user that is registeredin the authentication apparatus and an ECG signal associated with anunidentified user differing from the registered user. Thus, theauthentication apparatus acquires a plurality of output values 820 basedon the ECG signal associated with the registered user. Also, theauthentication apparatus acquires a plurality of output values 830, 840,or 850 based on the ECG signal associated with the unidentified user. Asa result, these output values may be considered for use in the exampleof FIG. 8. The ECG signal associated with the unidentified userincludes, for example, ECG signals associated with a plurality of usersdiffering from one another.

The authentication apparatus extracts second output values in a presetsimilarity range based on the first output value 810. In an example, theauthentication apparatus extracts a preset number of second outputvalues in an ascending order of similarities with the first output value810. For example, the example of FIG. 8 illustrates that 10 differentsecond output values are extracted as an example (i.e., the 6 outputvalues 820, 2 output values 830, 1 output value 940, and 1 output value850 inside the dashed circle) and thus, the number of extracted secondoutput values is not to be limited to such an example.

Additionally, the authentication apparatus calculates a ratio of thenumber of the extracted second output values 820 associated with theregistered user (6 in FIG. 8) to the total number of extracted secondoutput values (10 in FIG. 8). When the calculated ratio is higher thanratios of the numbers of the other extracted second output values 830,840, and 850 associated with other users (2, 1, and 1 in FIG. 8) to thetotal number of the extracted second output values, the authenticationapparatus authenticates the user in contact with the authenticationapparatus to be the registered user. For example, the ratio of thenumber of the extracted second output values 820 is calculated to be 60%( 6/10). The ratios of the numbers of the other extracted second outputvalues 830, 840, and 850 to the total number of the extracted outputvalues are each calculated to be 20% or 10% ( 2/10 or 1/10). Thus, theauthentication apparatus authenticates the user in contact with theauthentication apparatus to be the registered user.

In another example, when a ratio of the output values 820 associatedwith the registered user to the extracted second output values is higherthan or equal to a threshold, the authentication apparatus authenticatesthe user in contact with the authentication apparatus to the registereduser. In this example, the threshold is, for example, a value that isadjusted based on a security consideration for the authenticationapparatus. The authentication apparatus is potentially implemented invarious forms. For example, the authentication apparatus is a PC, alaptop computer, a tablet computer, a smartphone, a smart appliance, atelevision, an intelligence vehicle, and a wearable device. However,these are only examples of an authentication apparatus, and otherelectronic devices are used as the authentication apparatus in otherexamples. To be applied in, for example, a payment service based on ahigh security authentication, the threshold is adjusted to have arelatively high value. Without having a relatively high threshold, itcannot be reliably guaranteed that the authentication apparatus onlyauthenticates users that are actually legitimate users.

In the example of FIG. 8, a proportion of the output values 820 includedin the ten extracted second output values is 60%. When the threshold isset to, for example, 50%, the authentication apparatus authenticates theuser in contact with the authentication apparatus to be the registereduser, because there is sufficient evidence to determine thatauthentication should occur.

FIG. 9 is a block diagram illustrating another example of anauthentication apparatus in accordance with an embodiment.

Referring to the embodiment of FIG. 9, an authentication apparatus 900includes a calculator 910, an extractor 920, a determiner 930, and amemory 940. In the example of FIG. 9, the calculator 910 calculates afirst output value corresponding to an input ECG signal based on aneural network. Aspects of such calculation are discussed further,above. Also, the calculator 910 calculates a plurality of second outputvalues, each corresponding to a reference ECG signal based on the neuralnetwork. For example, the reference ECG signal is an ECG signalassociated with one of an unidentified user and a registered user. Thecalculator 910 calculates the first output value and the plurality ofsecond output values by applying a weight of the neural networkdetermined through a machine learning process, as discussed further,above. For example, the calculator 910 calculates the first output valueand the plurality of second output values by calculating a sum ofweights assigned to nodes in the neural network.

The extractor 920 extracts a preset number of second output values fromthe plurality of second output values based on the first output value.In an example, the extractor 920 extracts the preset number of secondoutput values in an ascending order of differences from the first outputvalue. The determiner 930 determines whether to authenticate a userbased on a ratio of the reference ECG signal corresponding to each ofthe extracted second output values to corresponding values for theregistered user. The determiner 930 authenticates a user associated withthe input ECG signal to be the registered user when a ratio of thereference ECG signal corresponding to each of the extracted secondoutput values to the values associated with the registered user ishigher or equal to a threshold. When the ratio is lower than athreshold, the determiner 930 outputs an authentication failure messageor a re-authentication request message to the user, because such aresult indicates that the ECG signal cannot be determined to besufficiently similar to characteristics of a registered user's ECGcharacteristics to confirm authentication.

In another example, the extractor 920 repetitively extracts secondoutput values in the ascending order of differences from the firstoutput value. In this example, the number of the extracted second outputvalues varies for each extraction. For example, the extractor 920repetitively extracts k₁ second output values, k₂ second output values,and k₃ second output values in the ascending order of differences fromthe first output value. In such an example, the determiner 930calculates a first ratio of a reference ECG signal corresponding to eachof the k₁ second output values to the corresponding values for theregistered user. Similarly, the determiner 930 calculates a second ratioof a reference ECG signal corresponding to each of the k₂ second outputvalues to the corresponding values for the registered user. Also, thedeterminer 930 calculates a third ratio of a reference ECG signalcorresponding to each of the k₃ second output values to thecorresponding values for the registered user. The determiner 930compares each of the first ratio, the second ratio, and the third ratioto a threshold. When the number of cases in which the ratios arecalculated to be higher than or equal to a threshold is larger than thenumber of cases in which the ratios are calculated to be lower than thethreshold, the determiner 930 authenticates the user associated with theinput ECG signal to be the registered user, as the comparison isindicative of sufficient similarity.

The memory 940 stores a reference ECG signal used for a userauthentication. For example, the memory 940 stores a plurality of ECGsignals corresponding to a plurality of different points in time of theregistered user as the reference ECG signal. These reference values areused for comparison when authenticating, as discussed above. Theauthentication apparatus 900 previously stores reference ECG signalscorresponding to various body states of the registered user to avoid anoverfitting result of machine learning. For example, as discussed above,the same user may have different ECG signals depending on, for example,whether the user has recently ingested caffeine. When a user in contactwith the authentication apparatus 900 is authenticated as the registereduser as discussed, the determiner 930 stores the input ECG signal in thememory 940 as a reference ECG signal associated with the registereduser, for use in subsequent authentications.

The apparatuses, units, modules, devices, and other componentsillustrated in FIGS. 1-9 that perform the operations described hereinwith respect to FIGS. 1-9 are implemented by hardware components.Examples of hardware components include controllers, sensors,generators, drivers, memories, comparators, arithmetic logic units,adders, subtractors, multipliers, dividers, integrators, neuralnetworks, signal separators, calculators, extractors, determiners, andany other electronic components known to one of ordinary skill in theart. In one example, the hardware components are implemented bycomputing hardware, for example, by one or more processors or computers.A processor or computer is implemented by one or more processingelements, such as an array of logic gates, a controller and anarithmetic logic unit, a digital signal processor, a microcomputer, aprogrammable logic controller, a field-programmable gate array, aprogrammable logic array, a microprocessor, or any other device orcombination of devices known to one of ordinary skill in the art that iscapable of responding to and executing instructions in a defined mannerto achieve a desired result. In one example, a processor or computerincludes, or is connected to, one or more memories storing instructionsor software that are executed by the processor or computer. Hardwarecomponents implemented by a processor or computer execute instructionsor software, such as an operating system (OS) and one or more softwareapplications that run on the OS, to perform the operations describedherein with respect to FIGS. 1-9. The hardware components also access,manipulate, process, create, and store data in response to execution ofthe instructions or software. For simplicity, the singular term“processor” or “computer” may be used in the description of the examplesdescribed herein, but in other examples multiple processors or computersare used, or a processor or computer includes multiple processingelements, or multiple types of processing elements, or both. In oneexample, a hardware component includes multiple processors, and inanother example, a hardware component includes a processor and acontroller. A hardware component has any one or more of differentprocessing configurations, examples of which include a single processor,independent processors, parallel processors, single-instructionsingle-data (SISD) multiprocessing, single-instruction multiple-data(SIMD) multiprocessing, multiple-instruction single-data (MISD)multiprocessing, and multiple-instruction multiple-data (MIMD)multiprocessing.

The methods illustrated in FIGS. 1-9 that perform the operationsdescribed herein with respect to FIGS. 1-9 are performed by computinghardware, for example, by one or more processors or computers, asdescribed above executing instructions or software to perform theoperations described herein.

Instructions or software to control a processor or computer to implementthe hardware components and perform the methods as described above arewritten as computer programs, code segments, instructions or anycombination thereof, for individually or collectively instructing orconfiguring the processor or computer to operate as a machine orspecial-purpose computer to perform the operations performed by thehardware components and the methods as described above. In one example,the instructions or software include machine code that is directlyexecuted by the processor or computer, such as machine code produced bya compiler. In another example, the instructions or software includehigher-level code that is executed by the processor or computer using aninterpreter. Programmers of ordinary skill in the art can readily writethe instructions or software based on the block diagrams and the flowcharts illustrated in the drawings and the corresponding descriptions inthe specification, which disclose algorithms for performing theoperations performed by the hardware components and the methods asdescribed above.

The instructions or software to control a processor or computer toimplement the hardware components and perform the methods as describedabove, and any associated data, data files, and data structures, arerecorded, stored, or fixed in or on one or more non-transitorycomputer-readable storage media. Examples of a non-transitorycomputer-readable storage medium include read-only memory (ROM),random-access memory (RAM), flash memory, CD-ROMs, CD-Rs, CD+Rs, CD-RWs,CD+RWs, DVD-ROMs, DVD-Rs, DVD+Rs, DVD-RWs, DVD+RWs, DVD-RAMs, BD-ROMs,BD-Rs, BD-R LTHs, BD-REs, magnetic tapes, floppy disks, magneto-opticaldata storage devices, optical data storage devices, hard disks,solid-state disks, and any device known to one of ordinary skill in theart that is capable of storing the instructions or software and anyassociated data, data files, and data structures in a non-transitorymanner and providing the instructions or software and any associateddata, data files, and data structures to a processor or computer so thatthe processor or computer can execute the instructions. In one example,the instructions or software and any associated data, data files, anddata structures are distributed over network-coupled computer systems sothat the instructions and software and any associated data, data files,and data structures are stored, accessed, and executed in a distributedfashion by the processor or computer.

While this disclosure includes specific examples, it will be apparent toone of ordinary skill in the art that various changes in form anddetails may be made in these examples without departing from the spiritand scope of the claims and their equivalents. The examples describedherein are to be considered in a descriptive sense only, and not forpurposes of limitation. Descriptions of features or aspects in eachexample are to be considered as being applicable to similar features oraspects in other examples. Suitable results may be achieved if thedescribed techniques are performed in a different order, and/or ifcomponents in a described system, architecture, device, or circuit arecombined in a different manner, and/or replaced or supplemented by othercomponents or their equivalents. Therefore, the scope of the disclosureis defined not by the detailed description, but by the claims and theirequivalents, and all variations within the scope of the claims and theirequivalents are to be construed as being included in the disclosure.

What is claimed is:
 1. An authentication apparatus comprising: acalculator configured to calculate a first output value corresponding toan input electrocardiogram (ECG) signal by applying the input ECG signalto a first neural network, and calculate second output valuescorresponding to reference ECG signals by applying the reference ECGsignals to a second neural network, wherein the first and second neuralnetworks are at least temporally implemented by one or more processors,the first output value is provided as output from the first neuralnetwork, and the second output values are provided as outputs from thesecond neural network; an extractor configured to extract a number ofsecond output values from the second output values based on the firstoutput value; and a determiner configured to determine whether toauthenticate a user associated with the input ECG signal based on aratio of a number of the extracted second output values that areassociated with a registered user to a total number of the extractedsecond output values.
 2. The authentication apparatus of claim 1,wherein the first and second neural networks share at least one of aconnection relationship of nodes, the number of internal nodes, a sizeof weight, and a size of a parameter.
 3. The authentication apparatus ofclaim 1, wherein the reference ECG signals comprises an ECG signalassociated with an unidentified user and an ECG signal associated withthe registered user, and the calculator is further configured tocalculate second output values corresponding to the unidentified userand the registered user.
 4. The authentication apparatus of claim 1,wherein the extractor is further configured to extract the number ofsecond output values in a descending order of similarities with thefirst output value.
 5. The authentication apparatus of claim 4, whereinthe extractor is further configured to repetitively extract secondoutput values from the second output values, a number of the extractedsecond output values being different for each extraction, andrepetitively calculate plural ratios from the repetitively extractingand the determiner is further configured to determine whether toauthenticate the user based on the plural ratios through the repetitivecalculation.
 6. The authentication apparatus of claim 1, wherein thedeterminer is further configured to authenticate the user associatedwith the input ECG signal to be the registered user in response to theratio of the number of the extracted second output values associatedwith the registered user to the total number of the extracted secondoutput values having a highest value.
 7. The authentication apparatus ofclaim 1, wherein the determiner is further configured to authenticatethe user associated with the input ECG signal to be the registered userin response to the ratio of the number of the extracted second outputvalues associated with the registered user to the total number of theextracted second output values being higher than or equal to athreshold.
 8. The authentication apparatus of claim 1, furthercomprising: a memory configured to store the reference ECG signals,wherein, in response to the user being authenticated as the registereduser, the determiner is further configured to store the input ECG signalin the memory as a part of the reference ECG signals in association withthe registered user.
 9. The authentication apparatus of claim 8, whereinthe memory is further configured to store ECG signals corresponding todifferent points in time as the reference ECG signals.
 10. A wearabledevice, comprising: a sensor configured to acquire an inputelectrocardiogram (ECG) signal from a body of a user in contact with thewearable device; a processor configured to: calculate a first outputvalue corresponding to the input ECG signal by applying the input ECGsignal to a first neural network, and calculate second output valuescorresponding to reference ECG signals by applying the reference ECGsignals to a second neural network, wherein the first output value isprovided as output from the first neural network and the second outputvalues are provided as outputs from the second neural network; extract anumber of second output values from the second output values based onthe first output value; and authenticate the user based on a ratio of anumber of the extracted second output values that are associated with aregistered user to a total number of the extracted second output values;and a display configured to output an authentication result to the user.11. The wearable device of claim 10, wherein the processor is furtherconfigured to extract the number of second output values from the secondoutput values in a descending order of similarities with the firstoutput value.
 12. The wearable device of claim 10, wherein the processoris further configured to determine the user to be the registered user inresponse to the ratio of the extracted second output values associatedwith the registered user being higher than or equal to a threshold.